Excess All-Cause Mortality in China After Ending the Zero COVID Policy

This cohort study estimates excess mortality among adults aged 30 years or older following the end of China’s zero COVID policy.


Introduction
During the first 3 years of the pandemic, China experienced low COVID-19-related excess mortality due to the implementation of stringent mitigation measures. 1 However, after China ended its dynamic zero COVID policy in December 2022, COVID-19 incidence and hospitalizations surged. 2 It has been reported by the Chinese government that approximately 60 000 COVID-19-related deaths occurred in health facilities in China from early December 2022 to January 12, 2023. 2 Prior forecasts had anticipated a notably higher number of excess deaths if the zero COVID policy were to be abandoned during the Omicron surge, ranging from 0.97 million to 2.10 million. [3][4][5][6][7] However, those model-based forecasts of excess deaths lacked an empirical basis.

Methods Data
Mortality information was derived from published obituary data for Peking University (PKU) and Tsinghua University (THU) in Beijing and Harbin Institute of Technology (HIT) in Harbin (Heilongjiang province) from January 1, 2016, to January 31, 2023. The number of employees, including current and retired staff, of the 3 universities as of 2022 were 19 992, 19 898, and 7293, respectively. The university published the obituary of each deceased official employee on its website, with an average delay of about 3 days after the date of death. Importantly, this process occurred both before and during the COVID-19 pandemic in a consistent fashion. The obituaries were extended to all deceased official employees, regardless of their age, sex, position (eg, professor, researcher, technician, librarian, and administrative staff), and employment status (ie, currently employed or retired). Our analysis did not include employees from the affiliated hospitals of the 3 universities because their obituaries were not published on the universities' websites.
Syndromic surveillance data were collected through search queries from Baidu, a Chinese internet search engine. The Baidu index (BI) is the weighted frequency of unique searches for a given keyword relative to the total search volume on Baidu. 8 The number of internet users in China exceeded 1 billion as of March 2023, and Baidu search's penetration rate reached over 90% among internet search engine users. 9, 10 The BI has been widely used as a data source for infodemiology and infoveillance studies, particularly during outbreaks of infectious disease. [11][12][13][14] Daily BI values for mortality-related Chinese terms for "funeral parlour (殡仪馆[Bin Yi Guan])," "cremation (⽕葬[Huo Zang])," "crematorium (⽕葬场[Huo Zang Chang])," and "burial (⼟葬[Tu Zang])" in each region (22 provinces, 4 municipalities, and 5 autonomous regions) of mainland China from January 1, 2016, to January 31, 2023, were obtained (https://index.baidu.com/v2/index.html#/). This study was exempt from institutional review approval owing to the use of published literature and publicly available data.

Statistical Analysis
We estimated the relative change in mortality among individuals 30 years and older in Beijing and Harbin from December 2022 to January 2023 using an interrupted time-series design, a quasiexperimental design widely used to assess the causal impact of shocks or interventions introduced at a distinct point in time. 15 We used a segmented negative binomial regression model, separating the time series into 3 periods: a pre-COVID-19 period (January 2016-December 2019), a period with stringent mitigation measures (January 2020-November 2022), and a post-zero COVID policy period (December 2022-January 2023). We included a linear association of time to capture the long-term secular trend of mortality rate. The negative binomial model equation (Equation 1) estimating monthly counts of deaths was specified as: E(ln(Death)) = β 0 + β 1 Month + β 2 COVID + β 3 ZeroCovid + offset(ln(P)).
Here, Death represents the monthly count of deaths, Month is the time (in months) since the start of the study period, COVID is an indicator variable indicating whether time occurred prior to or after the start of the COVID-19 pandemic (coded as 1 for months occurring after December 2019, and 0 otherwise), ZeroCovid is an indicator variable indicating whether time occurred prior to or after the end of the zero COVID policy (coded as 1 for months occurring after November 2022, and 0 otherwise), and P represents the catchment population (number of employees) in Month t . Newey-West standard errors with autocorrelation of 1 lag were used.
Similarly, we estimated the relative change in the BI associated with the lifting of the zero COVID policy in each region in China. The negative binomial model equation (Equation 2) estimating the daily BI was specified as: E(ln(BI i )) = β 0 + β 1 Day + β 2 COVID + β 3 ZeroCovid.
Here, BI i represents the daily BI in region i, Day is the time (in days) since the start of the study period, COVID is an indicator variable indicating whether time occurred prior to or after the start of the COVID-19 pandemic (coded as 1 for days occurring after December 2019, and 0 otherwise), and ZeroCovid is an indicator variable indicating whether time occurred prior to or after the end of the zero COVID policy (coded as 1 for days occurring after November 2022, and 0 otherwise). Newey-West standard errors with autocorrelation of 1 lag were used.
We observed a strong positive correlation (Beijing: r = 0.95, P < .001; Heilongjiang: r = 0.97, P < .001) between the change in BI for mortality-related terms and that in mortality due to relaxed zero COVID policies (Figure 1). Additionally, similar patterns in the change of BI for mortality-related terms were observed in all regions of China (eFigure 1 in Supplement 1). Therefore, the relative increase in mortality in the reference region (Beijing and Heilongjiang) was extrapolated to the rest of China assuming this same proportional association (Equation 3):

Here, (BI[RR ref ] − 1) and (BI[RR i ] − 1) represent the estimated relative change in BI using Equation 2
for the reference region and region i, respectively. The estimated relative change in mortality rate for Here, EM i is the excess mortality for region i, and Death i is the expected number of deaths for region i in December and January. The expected number of deaths by region and month was derived from the 2020 census data 16 and China National Disease Surveillance Points. 17 Additionally, sensitivity analyses were conducted using the leave-one-out method; that is, in 3 additional analyses, we iteratively excluded 1 of the 3 universities. In both the primary and sensitivity analyses, parameter uncertainty was incorporated by randomly drawing 10 000 samples from each parameter distribution and propagating this uncertainty forward through each step of the analysis.  of deaths in the 85-years-and-older age group in the prepandemic period and the first 3 years of the pandemic ( Table 1). The age and sex distributions among the deaths were similar in HIT (Table 1).
In both cities, death counts peaked in the fourth week of December 2022, concurrent with the highest BI in most provinces on December 25 (Figure 1; eFigure 1 in Supplement 1). The number of deaths in universities in Beijing showed a substantial increase compared with expected deaths, with a rise of 403% (95% CI, 351%-461%) and 56% (95% CI, 41%-73%) during December 2022 and January 2023, respectively (Table 1, Figure 1). Similarly, observed deaths in HIT were statistically significantly higher than the expected deaths both in December 2022 (12 vs 3; P < .001) and January 2023 (19 vs 3; P < .001).
The validity of our model was supported by examining whether placebo search terms (that is, search terms that are not expected to be related to the lifting of the zero COVID policy) also increased concurrently with mortality-related search terms. We found no evidence that this occurred, suggesting that mortality-related search terms through the BI served as valid surrogate for increased mortality (eFigure 2 in Supplement 1).   Figure 2). Estimates for excess deaths were generally consistent in the specified leaveone-out analyses (Figure 3).

Discussion
We estimated   a Demographic information derived from obituaries at THU was not obtainable. The overall death count (mortality rate) at THU during the 3 prespecified periods was 214 (23.7/10 000 person-months), 396 (57.9/10 000 person-months), and 92 (231.5/ 10 000 person-months), respectively. The monthly count of deaths at PKU and HIT was determined using the date of death, while the monthly count of deaths at THU was calculated based on the date that obituary was published. The median (IQR) of the difference between the date of obituary publications and the date of death was 1 (1-3), 2 (1-4), and 1 (0-3) days in PKU, THU, and HIT, respectively.
b Death counts instead of mortality rate for each subgroup were used in the table because data regarding the number of employees by demographic features (eg, sex and age) were not available.
COVID-19 deaths (0.71 per 1000 population) if the entire China population were infected after abandoning zero COVID policy. Our higher estimate than some forecasters may represent a greater effect of the SARS-CoV-2 virus on a population with limited immunity than anticipated. 3,18

Strengths and Limitations
Our study is among the first to provide rigorously derived, empirical estimates about excess deaths in China after the lifting of the zero COVID policy. Given the absence of comprehensive, publicly available data from China, our novel strategy for estimating excess deaths is both timely and important on this topic of public health concern both in China and internationally and demonstrates how the strategic combination of data sources can provide insights into seemingly opaque public health research questions. However, our study has limitations. The reliance on obituary data for employees from 3 universities in Beijing and Heilongjiang could result in an overestimation of excess mortality because university employees were older than the general population, or alternatively, an